from deepautoencoder import StackedAutoEncoder from sklearn.preprocessing import MinMaxScaler as scaler # trX, teX, trY, teY = _read_split( # "../datasets/nd-data/boundary.csv", # read=1,oneHot=0) #Integrating smote with daego #perform smote at the intermediate stage of training via stacked denoising encoder from algorithms.utils import _read_dat trX, teX, trY, teY = _read_dat( "dataset/page-blocks0.dat",skip=15, read=1,oneHot=0) scaler=scaler() trX=scaler.fit_transform(trX) teX=scaler.fit_transform(teX) from mlxtend.tf_classifier import TfSoftmaxRegression trY=trY.astype(int) print trX.shape[1],"Input Feature Space" print "Enter Layers" layer=input() print "Enter the leyer no after smote to be performed" l_s=int(input())
import numpy as np
import warnings
from algorithms.utils import _read_split, _class_split
from algorithms.clf_utils import _clf_dtree, _clf_svm, _clf_mlp
from algorithms.smote import SMOTE
#Test the classifier performance using synthetic samples generated via SMOTE
#link to paper: https://www.jair.org/media/953/live-953-2037-jair.pdf
from algorithms.utils import _read_dat
trX, teX, trY, teY = _read_dat("dataset/page-blocks0.dat",
skip=15,
read=1,
oneHot=0)
# trX, teX, trY, teY = _read_split(
# "../datasets/nd-data/boundary.csv",
# read=1,oneHot=0)
X0, X1 = _class_split(trX, trY, oneHot=0)
warnings.filterwarnings("ignore", category=DeprecationWarning)
print "Enter oversampling percent"
P = int(input())
syn_X = SMOTE(X1, P, 5)
X1 = np.vstack((X1, syn_X))
X1 = np.column_stack((X1, np.ones(X1.shape[0])))
X0 = np.column_stack((X0, np.zeros(X0.shape[0])))
Xy = np.vstack((X0, X1))
np.random.shuffle(Xy)